Since the launch of the Earth's Technological Satellite (ERTS), now known as LANDSAT, in 1972, numerous software packages have been developed and used to visualize and analyze multispectral imagery (MSI) data. Since the vast majority of MSI data have been prepared in a form of a regular matrix where missing data points are extremely rare, users in general have had no problem in filling in missing data. Thus, such users do not need to examine whether given software has an option to handle irregular data points, or whether the method is appropriate.
With an increasing use of Light Detection and Ranging (LIDAR) and with the advancement of GPS as a navigation system since the 1980s, users of remotely sensed imagery must handle missing data points and must know whether the methods to generate LIDAR-based images for feature and object extraction are appropriate. Inherently, LIDAR data points are not regularly spaced, and non-edited, point cloud LIDAR elevation data do not lend themselves to filling in missing data points by interpolation. For example, a conventional linear interpolation usually means that the values for intermediate mid-point data points are estimated by using the average from the two end points. A more sophisticated technique is a curvilinear interpolation method that takes the slope between two points into account. A commonly used interpolation algorithm in many software packages is known as the Inverse Distance Weighted (IDW) algorithm.
The question in turn, is not which interpolation scheme is better, but rather, is it fundamentally inappropriate to interpolate LIDAR's elevation data. For example, consider three buildings on a straight line. The two buildings on either side are of elevation 100 feet and 200 feet, respectively. By a linear interpolation, the building located at the middle of these two buildings would be 150 feet. Suppose however that ten buildings are at varying distances for the to-be-estimated building height. Although the IDW or any other interpolation technique can be used to estimate the building height, the process is illogical, and can lead to discrepancies.
Fundamentally, “to interpolate or not to interpolate” is a question of whether the given data distribution follows a discrete function or a continuous function. If the data distribution follows a continuous function, interpolation is appropriate. But if the data distribution is fundamentally discrete, like elevation data of buildings, interpolation should not be applied.
Currently, no system offers a method to capture, characterize, and visualize fundamentally discrete distribution data, such as LIDAR data, and extends the process to include generic image data for object and feature extraction.
A survey of methods used by conventional image processing software packages such as Idrisi (Idrisi is a registered trademark of Trustees of Clark University), Erdas (Erdas is a registered trademark of Erdas, Inc.), Global Mapper, and ArcGIS (ArcGIS is a registered trademark of ENVIRONMENTAL SYSTEMS RESEARCH INSTITUTE, INC.) reveals that a general sequence to generate an image from a LIDAR data set in the format of common separated vector (csv) first generates a Triangulated Irregular Network (TIN) and then performs interpolation on the TIN by interpolation such as IDW to generate an image.
Since using interpolation algorithms to convert an irregular matrix to a regular matrix for image generation is inappropriate when the data follows a discrete distribution, it would be advantageous to devise a system that receives and converts irregular matrix data into regular matrix data without the use of interpolation.
It is also advantageous to devise such a system that can handle irregular matrix data in various forms of data-point resolution and spatial density distribution, such as LIDAR returns.
It is also advantageous to design the data capture and visualization system to handle not only LIDAR data, but also generic image data like multispectral data.
Once the new data capture and visualization system for object and feature extraction can incorporate both LIDAR and generic image data, it is also advantageous that the system performs geoimage registration without altering the original LIDAR and generic image data.
It is advantageous for a new system to capture, characterize and visualize data for object and feature extraction by using dynamically adjustable pixel resolutions and controlling the radius of the data capture spatial domain.
It is also advantageous to capture the characteristics of data within a predetermined spatial domain, to know minimum, maximum, average, range and other statistical measurements.
It is also advantageous to generalize the data capture, characterization, and visualization from LIDAR data to generic image data.
It is also advantageous for a system to provide a measurement tool to capture and visualize data characteristics at a region of interest (ROI) or location of interest (LOI).
It is also advantageous for a system to display the captured data characteristics.
It is also advantageous for a system to provide a graphical user interface (GUI) to perform the discussed functions with relative ease.
Once the multiple data characteristics image layers are generated, it is advantageous to generate one or more data signatures for object and feature extraction.
It is also advantageous to generate color composites from LIDAR-derived bands to simulate natural color and color infrared (CIR) bands derived from conventional multispectral imagery.
Once multiple signatures from a region of interest are generated, it is advantageous to develop a parameter adjustable rule set for object and feature extraction.
It is also advantageous for a system to perform parameter adjustment at the individual signature level in addition to a group level.
It is most advantageous for a system to perform parameter adjustment in various object extraction domains, such as the number of signatures, the signature matching level, the density matching level, the radius of the search area, and so on.
For object and feature extraction, as well as display, it is also advantageous for a system to perform fusion between LIDAR and other sources, such as multispectral imagery.
It is also advantageous for a system to generate the most generalized objects and feature signatures automatically.
It is also advantageous for a system to generate the image that captures the dominant object or feature in the input image set.
It is also advantageous for a system to perform object and feature extraction using matching digital number (DN) values that do not match closely with dominant ones.
It is also advantageous for a system to perform object and feature extraction by combining both user-specified and automatically generated spectral signatures.